Home // SIMUL 2012, The Fourth International Conference on Advances in System Simulation // View article
Authors:
Tristan Salque
Peter Riederer
Dominique Marchio
Keywords: Artificial neural networks; Room temperature prediction; Predictive control; Energy savings; Geothermal heat pump
Abstract:
The use of artificial neural networks in the field of building energy management has led to remarkable results over the recent years. In this study, the development of room temperature neural network models, to be used for predictive control of geothermal heat pump systems, is discussed. The training process, including the determination of optimal input data, algorithm and structure, is detailed. The prediction performance of the developed neural network is compared to linear ARX models. Simulated data used for training and validation is generated using the TRNSYS environment. The developed model is then implemented into a predictive controller for geothermal heat pumps systems. Simulation results showed that the predictive controller can provide up to 17% energy savings in comparison with conventional controllers.
Pages: 24 to 29
Copyright: Copyright (c) IARIA, 2012
Publication date: November 18, 2012
Published in: conference
ISSN: 2308-4537
ISBN: 978-1-61208-234-9
Location: Lisbon, Portugal
Dates: from November 18, 2012 to November 23, 2012